EMNLP – the conference on Empirical Methods for Natural Language Processing – was held this year in Copenhagen, the capital of the small state of Denmark. Nevertheless, this year’s conference had the largest attendance in EMNLP’s history.
The surge in attendance should not be too surprising, as it follows similarly frothy demand for other academic machine learning conferences, such as NIPS (which recently sold out before workshop authors could even submit their papers).
The EMNLP conference focuses on data-driven approaches to NLP, which really describes all work in NLP, so I suppose we can call it a venue for “very data-driven NLP”. It’s a popular conference, and the premier conference of ACL’s SIGDAT (ACL’s special interest group for linguistic data and corpus-based approaches to NLP).
This event went off without a hitch, with plenty of eating and socializing space in the vicinity. For 1200 people. Must’ve been a lot of hard work. Continue reading “A Random Walk Through EMNLP 2017”
By David Kale and Zachary Lipton
Starting Friday, August 18th and lasting two days, Northeastern University in Boston hosted the eighth annual Machine Learning for Healthcare (MLHC) conference. This year marked MLHC’s second year as a publishing conference with an archival proceedings in the Journal of Machine Learning Research (JMLR). Incidentally, the transition to formal publishing venue in 2016 coincided with the name change to MLHC from Meaningful Use of Complex Medical Data, denoted by the memorable acronym MUCMD (pronounced MUCK-MED).
From its beginnings at Children’s Hospital Los Angeles as a non-archival symposium, the meeting set out to address the following problem:
- Machine learning, even then, was seen as a powerful tool that can confer insights and improve processes in domains with well-defined problems and large quantities of interesting data.
- In the course of treating patients, hospitals produce massive streams of data, including vital signs, lab tests, medication orders, radiologic imaging, and clinical notes, and record many health outcomes of interest, e.g., diagnoses. Moreover, numerous tasks in clinical care present as well-posed machine learning problems.
- However, despite the clear opportunities, there was surprisingly little collaboration between machine learning experts and clinicians. Few papers at elite machine learning conferences addressed problems in clinical health and few machine learning papers were submitted to the elite medical journals.
Continue reading “A Pedant’s Guide to MLHC 2017”